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Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging.

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Brain connectome reproducibility is influenced by MRI scanner and tractography methods. Probabilistic tractography and consistent scanner use enhance structural brain connectivity mapping reliability.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Connectivity

Background:

  • Structural brain connectome disruptions are implicated in neurological and psychiatric disorders.
  • Diffusion Tensor Imaging (DTI) is commonly used but susceptible to methodological variations.
  • Understanding factors affecting connectome reproducibility is crucial for reliable research.

Purpose of the Study:

  • To evaluate the impact of methodological factors on structural brain connectome reproducibility.
  • To assess the influence of MRI scanner and tractography approaches on connectome mapping.
  • To determine the reliability of graph theory network measures derived from the structural connectome.

Main Methods:

  • Twenty healthy adults underwent three MRI scanning sessions (two on the same scanner, one on a different scanner).
  • T1-weighted and DTI sequences were acquired, followed by deterministic or probabilistic tractography.
  • Reproducibility of link weights and graph theory measures was assessed using intra-class correlation coefficients.

Main Results:

  • Connectome reproducibility was higher when data was acquired from the same MRI scanner.
  • Probabilistic tractography demonstrated greater reproducibility compared to deterministic methods.
  • Links between larger, anatomically closer regions of interest (ROIs) showed higher reproducibility.
  • Graph theory network measures exhibited high reproducibility across scanning sessions.

Conclusions:

  • Anatomical factors and tractography choices significantly impact structural connectome reproducibility.
  • Connectome mapping is a reproducible technique, especially for network architecture assessed by graph theory.
  • Future studies should consider these variables for accurate interpretation of structural connectome data.